[HTML][HTML] A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks
Background: There is a broad interest in deploying deep learning-based classification
algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) …
algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) …
Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans
The application of machine learning to tasks involving volumetric biomedical imaging is
constrained by the limited availability of annotated datasets of three-dimensional (3D) scans …
constrained by the limited availability of annotated datasets of three-dimensional (3D) scans …
Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability
X Zhou, S Kedia, R Meng, M Gerstein - PloS one, 2024 - journals.plos.org
The early detection of Alzheimer's Disease (AD) is thought to be important for effective
intervention and management. Here, we explore deep learning methods for the early …
intervention and management. Here, we explore deep learning methods for the early …
Brain age analysis and dementia classification using convolutional neural networks trained on diffusion mri: Tests in indian and north american cohorts
Deep learning models based on convolutional neural networks (CNNs) have been used to
classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans …
classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans …
[HTML][HTML] SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging data
We present SLIViT, a deep-learning framework that accurately measures disease-related
risk factors in volumetric biomedical imaging, such as magnetic resonance imaging (MRI) …
risk factors in volumetric biomedical imaging, such as magnetic resonance imaging (MRI) …
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume
Z Zhang, Y Mohsenzadeh - arXiv preprint arXiv:2408.15958, 2024 - arxiv.org
Current anomaly detection methods excel with benchmark industrial data but struggle with
natural images and medical data due to varying definitions of'normal'and'abnormal.'This …
natural images and medical data due to varying definitions of'normal'and'abnormal.'This …
Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers
Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss,
presents a formidable global health challenge, underscoring the critical importance of early …
presents a formidable global health challenge, underscoring the critical importance of early …
Inter-Slice Attention Transformer for Predicting Risk Level of Gastrointestinal Stromal Tumors
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the
gastrointestinal tract. GIST risk classification based on CT images is of great clinical …
gastrointestinal tract. GIST risk classification based on CT images is of great clinical …